The term noise suppression generally describes a signal processing technique that attempts to attenuate or remove an undesired noise component from an input signal. Noise suppression may be applied to almost any type of input signal that may include an undesired/interfering component such as a noise component. For example, noise suppression functionality is often implemented in telecommunications devices, such as telephones, Bluetooth® headsets, or the like, to attenuate or remove an undesired background noise component from an input speech signal. In general, an input speech signal may be viewed as comprising both a desired speech component (sometimes referred to as “clean speech”) and a background noise component. Removing the background noise component from the input speech signal ideally leaves only the desired speech component as output.
In multi-microphone systems, noise suppression is often implemented based on the Generalized Sidelobe Canceler (GSC). The GSC consists of a fixed beamformer, a blocking matrix, and an adaptive noise canceler. In the most general case, the fixed beamformer functions to filter M input speech signals received from M microphones to create a so-called speech reference signal comprising a desired speech component and a background noise component. The blocking matrix creates M−1 background noise references by spatially suppressing the desired speech component in the M input speech signals. The adaptive noise canceler then estimates the background noise component in the speech reference signal, produced by the fixed beamformer based on the M−1 background noise references and suppresses the estimated background noise component from the speech reference signal, thereby ideally leaving only the desired speech component as output.
However, in some multi-microphone systems, at least one microphone is dedicated as a noise reference microphone and at least one microphone is dedicated as a primary speech microphone. The noise reference microphone is positioned to be relatively far from a desired speech source during regular use of the multi-microphone system. In fact, the noise reference microphone can be positioned to be as far from the desired speech source as possible during regular use of the multi-microphone system. Therefore, the input speech signal received by the noise reference microphone often will have a very poor signal-to-noise ratio (SNR). The primary speech microphone, on the other hand, is positioned to be relatively close to the desired speech source during regular use and, as a result, usually receives an input speech signal that has a much better SNR compared to the input speech signal received by the noise reference microphone.
In these multi-microphone systems, with a dedicated noise reference microphone and primary speech microphone, the traditional delay-and-sum fixed beamformer structure of the GSC (described above) may not make much sense because it can result in a speech reference signal with an SNR that is worse than that of the unprocessed input speech signal received by the primary speech microphone. In general, it is possible to get constructive interference between the desired speech components of input speech signals received by multiple microphones using the traditional delay-and-sum fixed beamformer structure. However, in the case of a multi-microphone system with a noise reference microphone and a primary speech microphone as described above, the traditional delay-and-sum fixed beamformer structure is often unable to improve the SNR compared to the primary speech microphone because of the poor SNR of the input speech signal received by the noise reference microphone. Thus, using the traditional delay-and-sum fixed beamformer structure in such a multi-microphone system often will result in a speech reference signal that has a worse SNR than that of the input speech signal received by the primary speech microphone.
Moreover, adaptive algorithms (e.g., a least mean square adaptive algorithm) conventionally used to derive the filters for the blocking matrix and the adaptive noise canceler of the GSC are often slow to converge.
Therefore, what is needed is an approach to multi-channel noise suppression that does not rely on the traditional delay-and-sum fixed beamformer structure of the GSC and/or slow to converge adaptive algorithms for deriving filters used to suppress noise.
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